Director, AI Solutions & Integration

About The Role

Grade Level (for internal use)

13

The Team

The Director of AI Solutions & Integration leads the team that brings advanced analytics and AI capabilities into the heart of SPDJI's data operations. Reporting to the Director of Data AI & Enablement, this role ensures that AI is not just an experiment, but a production-ready, governed, and value-driving capability. This strategic leadership position enables SPDJI to harness the power of AI at scale, by accelerating innovation, enhancing productivity, and ensuring that every AI solution is safe, and aligned with business priorities.

Responsibilities And Impact

Strategic Leadership & AI Vision

Own and drive the AI solutions enablement strategy for SPDJI, defining how value streams design, build, and scale AI applications that deliver measurable business value
Establish reference architectures and standards for GenAI/LLM solutions (e.g., RAG patterns, agentic workflows, orchestration, integration into data pipelines), ensuring solutions are secure, governable, and production-ready
Define and implement enablement programs for GenAI, LLM, and agent-based solutions across the organization
Contribute to the broader Data AI & Enablement strategy, ensuring AI capabilities align with organizational strategic goals and data platform initiatives
Represent SPDJI Data at enterprise AI forums, advocating for AI capabilities and breakthrough innovations

AI Roadmap & Strategic Planning

Partner with PPD and value stream leadership to shape the AI roadmap, provide feasibility input, identify dependencies, and define clear success metrics and acceptance criteria for prioritized use cases
Collaborate with stakeholders to identify high-value AI opportunities across Equity, Fixed Income, and Multi-Asset domains
Balance innovation with pragmatism, ensuring AI investments deliver tangible business outcomes
Provide technical leadership on emerging AI technologies and their applicability to index management and data operations

Delivery Through Enablement

Co-develop AI applications with value stream SMEs, from prototyping to production deployment
Establish prompt engineering and evaluation practices including reusable prompt patterns, test harnesses, quality benchmarks, and regression approaches to prevent degradation over time
Oversee the design and implementation of end-to-end AI solutions including RAG pipelines, agentic workflows, and LLM-enabled applications

Governance & Responsible AI

Partner closely with the Data & AI Governance team to embed responsible AI practices and risk controls into every solution
Ensure all AI solutions are integrated with governed data, meet security and operational requirements
Establish and maintain frameworks for AI solution monitoring, evaluation, and continuous improvement

Production Readiness & IT Partnership

Ensure operational readiness for IT handover by driving standards for documentation, monitoring, cost controls, incident response expectations, and secure integration with enterprise platforms
Partner with IT to support QA processes and early production stabilization
Establish quality gates and "definition of done" criteria specific to AI solutions

Team Development & Capability Building

Lead, mentor, and develop a high-performing team of AI Solutions Leads and Experts
Develop team capability and reusable assets (starter kits, libraries, templates, office hours/workshops), accelerating adoption of AI patterns across the organization
Collaborate effectively with Data Integration & Workflows and Data Governance teams to ensure cohesive platform enablement

Shared Accountabilities

With PPD: Collaborate on AI roadmap prioritization and alignment with business requirements and strategic goals; provide realistic technical feasibility assessments and success metrics definition.
With IT: Partner to ensure infrastructure readiness for AI workloads, smooth deployment of production-ready AI solutions, and operational excellence; establish clear support boundaries and SLAs.
With Data Value Streams: Engage with value stream SMEs to co-develop AI solutions, ensuring alignment with business logic and domain expertise; assess and develop SME AI capabilities.
With Data & AI Governance: Collaborate closely to embed responsible AI controls, conduct safety reviews, and ensure compliance with AI usage policies and risk management frameworks.
With Data Integration & Workflows: Partner to operationalize AI data requirements, refresh cadences, and integration of AI solutions with data pipelines.

Ownership

AI Solutions Strategy: Own the technical strategy, standards, and execution approach for all AI and advanced analytics initiatives
AI Reference Architectures: Responsible for defining and maintaining reference architectures for GenAI, LLM, RAG, and agentic solutions
AI Production Readiness Framework: Define and enforce the "definition of done" for production-ready AI solutions

What Success Looks Like

Enable AI at Scale: Establish scalable patterns and frameworks that enable multiple value streams to deliver AI solutions efficiently and consistently
Ensure Responsible AI: Embed governance, security, and ethical considerations into every AI solution, maintaining organizational trust and compliance
Accelerate AI Innovation: Reduce time-to-value for AI use cases through reusable components, clear standards, and effective enablement
Drive Measurable Business Value: Ensure AI solutions deliver quantifiable business outcomes aligned with strategic priorities

Key Performance Indicators (KPIs)

Business Value Realization: Measurable business impact (productivity gains, cost savings, quality improvements) from deployed AI solutions
Production Stability: Incident rate, performance metrics, and cost efficiency of AI solutions in production
Time to Production: Reduction in time from AI prototype to production-ready solutions

Compensation/Benefits Information: (This section is only applicable to US candidates)

S&P Global st